Predicting Companies Delisting to Improve Mutual Fund Performance

Project Details


Fall 2014


Ta-Wei Huang, Eugene Yang, Po-Wei Huang





Stock is removed from an exchange because the company for which the stock is issued, whether voluntarily or involuntarily, is not in compliance with the listing requirements of the exchange. Companies that are delisted are not necessarily bankrupt, but most of bankrupt company will be finally delisted from the exchange. To earn extra high returns on the stock market, mutual fund managers in Taiwan sometimes invest in high risk companies that might to be delisted in one year. However, once those companies get delisted, mutual funds managers will suffer from significant losses because most of those companies will confront a drastic decline in stock prices before delisted from the exchange.

To prevent mutual funds managers from investing in those potentially-delisted stocks, it is definitely very useful to build a system that predict whether a company will be delisted after one years. Therefore, our goal is to predict whether a company would be delisted in one year. We use 2012 financial reports of non-deliested companies and financial reports of companies one year before its delisted in Taiwan stock market to derive a supervised classification model predicting whether the company will be delisted in 1 year.

By trying K-nearest neighbors, ada-boosting classification tree, and logistic regression and embedding a cost function, we finally chose the logistic regression with cutoff probability 0.65 as our final model. We also use the “130-30” portfolio strategy to compare our prediction result with the market, and we outperform the market in expected return. However, we could not guarantee the stableness of this model during the financial crisis. Investors should still be aware of major economic situations that could cause the model fail. In addition, investor’s psychology could also misuse this model and self-fill the delist of a misclassified company.

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